404 citations · 712 across the 15 of their papers we have counts for
16 papers
Knowledge Graph Completion with Counterfactual Augmentation
Heng Chang, Jie Cai, Jia Li
Graph Neural Networks (GNNs) have demonstrated great success in Knowledge Graph Completion (KGC) by modeling how entities and relations interact in recent years. However, most of t…
Generating Negative Samples for Sequential Recommendation
Yongjun Chen, Jia Li, Zhiwei Liu +4
To make Sequential Recommendation (SR) successful, recent works focus on designing effective sequential encoders, fusing side information, and mining extra positive self-supervisio…
MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation
Wenzhuo Yang, Jia Li, Caiming Xiong +1
Counterfactual explanation is an important Explainable AI technique to explain machine learning predictions. Despite being studied actively, existing optimization-based methods oft…
Rethinking Graph Neural Networks for Anomaly Detection
Jianheng Tang, Jiajin Li, Ziqi Gao +1
Graph Neural Networks (GNNs) are widely applied for graph anomaly detection. As one of the key components for GNN design is to select a tailored spectral filter, we take the first…
ELECRec: Training Sequential Recommenders as Discriminators
Yongjun Chen, Jia Li, Caiming Xiong
Sequential recommendation is often considered as a generative task, i.e., training a sequential encoder to generate the next item of a user's interests based on her historical inte…
Improving Contrastive Learning with Model Augmentation
Zhiwei Liu, Yongjun Chen, Jia Li +3
The sequential recommendation aims at predicting the next items in user behaviors, which can be solved by characterizing item relationships in sequences. Due to the data sparsity a…
ConTinTin: Continual Learning from Task Instructions
Wenpeng Yin, Jia Li, Caiming Xiong
The mainstream machine learning paradigms for NLP often work with two underlying presumptions. First, the target task is predefined and static; a system merely needs to learn to so…
Styleverse: Towards Identity Stylization across Heterogeneous Domains
Jia Li, Jie Cao, JunXian Duan +1
We propose a new challenging task namely IDentity Stylization (IDS) across heterogeneous domains. IDS focuses on stylizing the content identity, rather than completely swapping it…
Intent Contrastive Learning for Sequential Recommendation
Yongjun Chen, Zhiwei Liu, Jia Li +2
Users' interactions with items are driven by various intents (e.g., preparing for holiday gifts, shopping for fishing equipment, etc.).However, users' underlying intents are often…
RGRecSys: A Toolkit for Robustness Evaluation of Recommender Systems
Zohreh Ovaisi, Shelby Heinecke, Jia Li +3
Robust machine learning is an increasingly important topic that focuses on developing models resilient to various forms of imperfect data. Due to the pervasiveness of recommender s…
Combining Data-driven Supervision with Human-in-the-loop Feedback for Entity Resolution
Wenpeng Yin, Shelby Heinecke, Jia Li +7
The distribution gap between training datasets and data encountered in production is well acknowledged. Training datasets are often constructed over a fixed period of time and by c…
Modeling Dynamic Attributes for Next Basket Recommendation
Yongjun Chen, Jia Li, Chenghao Liu +4
Traditional approaches to next item and next basket recommendation typically extract users' interests based on their past interactions and associated static contextual information…
Contrastive Self-supervised Sequential Recommendation with Robust Augmentation
Zhiwei Liu, Yongjun Chen, Jia Li +3
Sequential Recommendationdescribes a set of techniques to model dynamic user behavior in order to predict future interactions in sequential user data. At their core, such approache…
CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers
Shiyang Li, Semih Yavuz, Kazuma Hashimoto +6
Dialogue state trackers have made significant progress on benchmark datasets, but their generalization capability to novel and realistic scenarios beyond the held-out conversations…
Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT
Lichao Sun, Kazuma Hashimoto, Wenpeng Yin +4
There is an increasing amount of literature that claims the brittleness of deep neural networks in dealing with adversarial examples that are created maliciously. It is unclear, ho…
Private Deep Learning with Teacher Ensembles
Lichao Sun, Yingbo Zhou, Ji Wang +4
Privacy-preserving deep learning is crucial for deploying deep neural network based solutions, especially when the model works on data that contains sensitive information. Most pri…